Client Knowledge Assistant Build on a Vector Retrieval Layer (10-18 days)
A productized engagement that stands up a semantic retrieval layer over a client's scattered content, then ships a working knowledge assistant and a measurable retrieval quality baseline. Agencies sell the outcome (accurate answers, cited sources, lower support load) rather than a database license. Time: 10-18 days.
By InnovaAI ResearchPublished
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Client Knowledge Assistant Build on a Vector Retrieval Layer (10-18 days)
A productized engagement that stands up a semantic retrieval layer over a client's scattered content, then ships a working knowledge assistant and a measurable retrieval quality baseline. Agencies sell the outcome (accurate answers, cited sources, lower support load) rather than a database license.
- A signed scope naming one client business unit and a single high-value question set (support deflection, sales enablement, or internal onboarding).
- 1.Kickoff with the client owner and confirm the question set the assistant must answer
- 2.Inventory every content source and record volume, format, and update cadence
- 3.Agree the retrieval quality bar in writing (for example, correct answer in top three results)
- 1.Choose the deployment model: managed platform, self-hosted engine, or vector search inside an existing operational database
- 2.Document the lock-in tradeoff for the client in plain language
- 3.Confirm the embedding model and chunking strategy
- 1.Build the ingestion pipeline from source systems into the chosen platform
- 2.Strip duplicate and superseded documents before embedding
- 3.Log a manifest of every document ingested with its source timestamp
- 1.Run the first full embedding pass and record cost and wall-clock time
- 2.Attach metadata to each chunk (owner, product line, effective date, confidentiality tier)
- 3.Verify metadata filtering returns the expected subset on three test queries
- 1.Stand up the retrieval endpoint and connect it to the assistant interface
- 2.Add source citations to every generated answer
- 3.Test ten questions from the agreed set and log failures
- 1.Tune chunk size and overlap against the failure log
- 2.Add a reranking step if precision is below the agreed bar
- 3.Re-run the ten-question test and compare results
- 1.Run the relevance grading session with client subject-matter experts
- 2.Score each answer for correctness, completeness, and citation accuracy
- 3.Freeze the question set as the regression suite
- 1.Add a human-review checkpoint for any answer touching pricing, legal, or client-facing commitments
- 2.Define the escalation path when retrieval confidence is low
- 3.Document the autonomy boundary for the assistant in the client's own policy language
- 1.Instrument query volume, latency, and answer acceptance rate
- 2.Build the weekly retrieval quality report the client will own after handover
- 3.Set alert thresholds for stale content and empty-result queries
- 1.Write the runbook covering re-indexing, content updates, and incident response
- 2.Record a 20-minute walkthrough for the client's internal team
- 3.Hand over credentials and confirm access for two client staff
- 1.Run a 30-day forward plan for content refresh and question-set expansion
- 2.Price the retainer tier that covers monitoring and monthly re-indexing
- 3.Present results to the client sponsor with the baseline and target metrics
The billable work is retrieval engineering and evaluation, not database administration, so the agency prices against the client's cost of wrong answers: support tickets, slow onboarding, and lost sales time. Once the ingestion pipeline and regression suite exist, monthly re-indexing and quality reporting become a recurring retainer with low marginal delivery cost. Agencies that publish a measured accuracy baseline can defend premium pricing because the client can see the before and after numbers rather than a feature list.
- Ingestion pipeline with a document manifest and metadata schema
- Working knowledge assistant with source citations and a human-review checkpoint
- Retrieval quality baseline report scored by client subject-matter experts
- Operations runbook covering re-indexing, content refresh, and incident response
- 30-day forward plan with a retainer tier and named owner on the client side
The client's named owner can update a source document, trigger re-indexing without agency help, and the assistant returns a correctly cited answer to at least 80% of the frozen question set on the next scheduled run.